Rebecca Gosling
NIHR Clinical Lecturer
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About
Rebecca Gosling is an NIHR Clinical Lecturer at the University of Sheffield, UK. Her research interests include virtual physiological analysis, coronary hemodynamics, and the impact of computational fluid dynamics on cardiovascular conditions. Recent articles by Dr. Gosling explore topics such as the evaluation of coronary artery flow models and the determinants of trans-prosthetic gradients in aortic valve replacement.
Articles (16)
Year:
2025
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Background and objectives: Evaluating left ventricular filling pressure (LVFP) plays a crucial role in diagnosing and managing heart failure (HF). While traditional assessment methods involve multi-parametric transthoracic echocardiography (TTE) or right heart catheterisation (RHC), cardiovascular magnetic resonance (CMR) has emerged as a valuable diagnostic tool in HF. This study aimed to assess a simple CMR-derived model to estimate pulmonary capillary wedge pressure (PCWP) in a cohort of patients with suspected or proven heart failure and to investigate its performance in risk-stratifying patients. Materials and methods: A total of 835 patients with breathlessness were evaluated using RHC and CMR and split into derivation (85%) and validation cohorts (15%). Uni-variate and multi-variate linear regression analyses were used to derive a model for PCWP estimation using CMR. The model’s performance was evaluated by comparing CMR-derived PCWP with PCWP obtained from RHC. Results: A CMR-derived PCWP incorporating left ventricular mass and the left atrial area (LAA) demonstrated good diagnostic accuracy. The model correctly reclassified 66% of participants whose TTE was ‘indeterminate’ or ‘incorrect’ in identifying raised filling pressures. On survival analysis, the CMR-derived PCWP model was predictive for mortality (HR 1.15, 95% CI 1.04–1.28, p = 0.005), which was not the case for PCWP obtained using RHC or TTE. Conclusions: The simplified CMR-derived PCWP model provides an accurate and practical tool for estimating PCWP in patients with suspected or proven heart failure. Its predictive value for mortality suggests the ability to play a valuable adjunctive role in echocardiography, especially in cases with unclear echocardiographic assessment.
Year:
2023
Year:
2023
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2022
Collaborators (10)
Yuanlin Gu
Lecturer in Computer Science
University of Roehampton
Gareth Matthews
University of East Anglia
Julian Gunn
Professor of Interventional Cardiology
University of Sheffield
Robert Storey
University of Sheffield
Eylem Levelt
University of Leeds Faculty of Biological Sciences
Ciaran Grafton-Clarke
University of East Anglia
Hosamadin S Assadi
University of East Anglia
Peter P Swoboda
University of Leeds
Paul Morris
Wellcome Fellow, Senior Clinical Lecturer and Hon Consultant Cardiologist
University of Sheffield
Pankaj Garg
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